Instructions to use J-LAB/BRisa-7B-Instruct-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use J-LAB/BRisa-7B-Instruct-v0.2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="J-LAB/BRisa-7B-Instruct-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("J-LAB/BRisa-7B-Instruct-v0.2")
model = AutoModelForMultimodalLM.from_pretrained("J-LAB/BRisa-7B-Instruct-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use J-LAB/BRisa-7B-Instruct-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "J-LAB/BRisa-7B-Instruct-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "J-LAB/BRisa-7B-Instruct-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/J-LAB/BRisa-7B-Instruct-v0.2

SGLang

How to use J-LAB/BRisa-7B-Instruct-v0.2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "J-LAB/BRisa-7B-Instruct-v0.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "J-LAB/BRisa-7B-Instruct-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "J-LAB/BRisa-7B-Instruct-v0.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "J-LAB/BRisa-7B-Instruct-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use J-LAB/BRisa-7B-Instruct-v0.2 with Docker Model Runner:
```
docker model run hf.co/J-LAB/BRisa-7B-Instruct-v0.2
```

BRisa-7B-Instruct-v0.2 / README.md

leaderboard-pt-pr-bot

Adding the Open Portuguese LLM Leaderboard Evaluation Results

b9022cb verified about 2 years ago

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	---
	license: apache-2.0
	tags:
	- JJhooww/Mistral-7B-v0.2-Base_ptbr
	- J-LAB/BRisa
	model-index:
	- name: BRisa-7B-Instruct-v0.2
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: ENEM Challenge (No Images)
	type: eduagarcia/enem_challenge
	split: train
	args:
	num_few_shot: 3
	metrics:
	- type: acc
	value: 65.08
	name: accuracy
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=J-LAB/BRisa-7B-Instruct-v0.2
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: BLUEX (No Images)
	type: eduagarcia-temp/BLUEX_without_images
	split: train
	args:
	num_few_shot: 3
	metrics:
	- type: acc
	value: 53.69
	name: accuracy
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=J-LAB/BRisa-7B-Instruct-v0.2
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: OAB Exams
	type: eduagarcia/oab_exams
	split: train
	args:
	num_few_shot: 3
	metrics:
	- type: acc
	value: 43.37
	name: accuracy
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=J-LAB/BRisa-7B-Instruct-v0.2
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: Assin2 RTE
	type: assin2
	split: test
	args:
	num_few_shot: 15
	metrics:
	- type: f1_macro
	value: 91.5
	name: f1-macro
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=J-LAB/BRisa-7B-Instruct-v0.2
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: Assin2 STS
	type: eduagarcia/portuguese_benchmark
	split: test
	args:
	num_few_shot: 15
	metrics:
	- type: pearson
	value: 73.61
	name: pearson
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=J-LAB/BRisa-7B-Instruct-v0.2
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: FaQuAD NLI
	type: ruanchaves/faquad-nli
	split: test
	args:
	num_few_shot: 15
	metrics:
	- type: f1_macro
	value: 68.31
	name: f1-macro
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=J-LAB/BRisa-7B-Instruct-v0.2
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: HateBR Binary
	type: ruanchaves/hatebr
	split: test
	args:
	num_few_shot: 25
	metrics:
	- type: f1_macro
	value: 74.28
	name: f1-macro
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=J-LAB/BRisa-7B-Instruct-v0.2
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: PT Hate Speech Binary
	type: hate_speech_portuguese
	split: test
	args:
	num_few_shot: 25
	metrics:
	- type: f1_macro
	value: 65.12
	name: f1-macro
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=J-LAB/BRisa-7B-Instruct-v0.2
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: tweetSentBR
	type: eduagarcia/tweetsentbr_fewshot
	split: test
	args:
	num_few_shot: 25
	metrics:
	- type: f1_macro
	value: 60.77
	name: f1-macro
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=J-LAB/BRisa-7B-Instruct-v0.2
	name: Open Portuguese LLM Leaderboard
	---

	# BRisa 7B Instruct

	This is an instruction model trained for good performance in Portuguese. The initial base is the Mistral 7B v2 Model ([source](https://huggingface.co/mistral-community/Mistral-7B-v0.2)). We utilized the JJhooww/Mistral-7B-v0.2-Base_ptbr version pre-trained on 1 billion tokens in Portuguese ([source](https://huggingface.co/JJhooww/Mistral-7B-v0.2-Base_ptbr)).

	The base model has good performance in Portuguese but faces significant challenges following instructions. We therefore used the version mistralai/Mistral-7B-Instruct-v0.2 and fine-tuned it for responses in Portuguese, then merged it with the base JJhooww/Mistral-7B-v0.2-Base_ptbr (https://huggingface.co/JJhooww/Mistral-7B-v0.2-Base_ptbr).

	- Developed by: ([J-LAB](https://huggingface.co/J-LAB/))
	- Language(s) (NLP): Portuguese
	- License: APACHE
	- Finetuned from model: ([source](https://huggingface.co/JJhooww/Mistral-7B-v0.2-Base_ptbr))

	### Model Sources

	- Demo: ([Demonstracao da Versão DPO](https://huggingface.co/spaces/J-LAB/BRisa-7B))


	# Open Portuguese LLM Leaderboard Evaluation Results

	Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/J-LAB/BRisa-7B-Instruct-v0.2) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard)

	\| Metric \| Value \|
	\|--------------------------\|---------\|
	\|Average \|66.19\|
	\|ENEM Challenge (No Images)\| 65.08\|
	\|BLUEX (No Images) \| 53.69\|
	\|OAB Exams \| 43.37\|
	\|Assin2 RTE \| 91.50\|
	\|Assin2 STS \| 73.61\|
	\|FaQuAD NLI \| 68.31\|
	\|HateBR Binary \| 74.28\|
	\|PT Hate Speech Binary \| 65.12\|
	\|tweetSentBR \| 60.77\|